Numerical Methods and Computer Programming syllabus

Python Basics: Data types, control statements, loops, functions, modules, file handling. Libraries: Introduction to NumPy, Matplotlib, SymPy and SciPy for scientific computation. Errors in Computation: Round-off and truncation errors and error sources. Overview of Numerical Methods: Need and scope of numerical methods in multidisciplinary domains. Importance in electrical engineering applications (circuit analysis, control systems, electromagnetics, etc.) Integrated Activities: Online module: Python basics via any MOOCs Real-life error examples (e.g., incorrect meter readings)

Bisection Method, False Position (Regula Falsi) Method, Newton-Raphson Method (for one variable), Rate of Convergence and limitations of methods. Engineering Applications: Solving nonlinear equations in electrical circuits (e.g., diode IV characteristics, motor speed equations) Skill Integration: Develop reusable Python modules for root-finding Small group activity: Select and solve an interdisciplinary real-world nonlinear problem

Direct Methods: Gauss Elimination, Partial Pivoting, Gauss-Jordan, LU Decomposition. Iterative Methods: Jacobi Method, Gauss-Seidel Method. Applications: Power system equations, network analysis: Solving node-voltage and mesh- current equations in electric circuits. Practical Integration: Solve nodal/mesh equations using NumPy Team coding challenge: Solve a large circuit matrix and plot results

Interpolation: Newton’s Forward and Backward Difference, Lagrange Interpolation. Numerical Differentiation: Using finite differences for equally spaced points. Numerical Integration: Trapezoidal Rule, Simpson’s 1/3 Rule, Simpson’s 3/8 Rule (for single & double integration). Applications: Area under waveforms, energy calculations in electrical signals. 21st Century Skill Component Python visualization using Matplotlib Analyze sensor data or waveform signals from IoT kits (if available)

Solution of First Order ODEs: Euler’s Method, Modified Euler Method, Runge-Kutta Methods (2 & 4th order). nd Curve Fitting: Least Squares Method (Linear and 2nd Order). Applications: Transient analysis of RL, RC, and RLC circuits; fitting performance curves for machines. Project Integration: Build and simulate electrical system models Data fitting for experimental datasets (motor torque-speed curve, IV curves)